Objectives: The unprecedented COVID-19 pandemic has intensified the stress on blood banks and deprived the blood sources due to the containment measures that restrict the movement and travel limitations among blood donors. During this time, Malaysia had a significant 40% reduction in blood supply. Blood centers and hospitals faced a huge challenge balancing blood demand and collection. The health care systems need a proactive plan to withstand the uncertain situation such as the COVID-19 pandemic. This study investigates the psychosocial factors that affect blood donation behavior during a pandemic and aims to propose evidence-based strategies for a sustainable blood supply. Study design: Qualitative design using focus group discussion (FGD) was employed. Methods: Data were acquired from the two FGDs that group from transfusion medicine specialists (N = 8) and donors (N = 10). The FGD interview protocol was developed based on the UTM Research Ethics Committee’s approval. Then, the data was analyzed using Nvivo based on the General Inductive Approach (GIA). Results: Analysis of the text data found that the psychology of blood donation during the pandemic in Malaysia can be classified into four main themes: (i) reduced donation; (ii) motivation of donating blood; (iii) trends of donation; and (iv) challenges faced by the one-off, occasional, and non-donors. Conclusions: Based on the emerging themes from the FGDs, this study proposes four psycho-contextual strategies for relevant authorities to manage sustainable blood accumulation during the pandemic: (1) develop standard operating procedure for blood donors; (2) organize awareness campaigns; (3) create a centralized integrated blood donors database; and (4) provide innovative Blood Donation Facilities.

In this research, we employed multivariate statistical methods to investigate the perspectives of small and medium-sized enterprises (SMEs) concerning the Extended Producer Responsibility (EPR) regulation and their apprehensions related to EPR compliance. The EPR regulation, which places the responsibility of waste management on producers, has significant financial and administrative implications, particularly for SMEs. A sample of 114 businesses was randomly selected, and the collected data underwent comprehensive analysis. Our findings highlight that a notable proportion of businesses (44.7%) possess knowledge of the EPR regulation’s provisions, whereas only a marginal fraction (1.8%) lacks sufficient familiarity. We also explored the interplay between opinions on the EPR regulation and concerns regarding its financial and administrative implications. Our results establish a significant correlation between EPR regulation opinions and concerns, with adverse opinions prominently influencing concerns, particularly regarding financial burdens and administrative workloads. These outcomes, derived from the application of multivariate statistical techniques, provide valuable insights for enhancing the synergy between environmental regulations and business practices. EPR regulation significantly affects SMEs in terms of financial, administrative, and legal obligations, thus our study highlights that policymakers may need to consider additional support mechanisms to alleviate the regulatory burden on SMEs, fostering a more effective and sustainable implementation of the EPR regulation.

The pressing need to redefine the tourism industry's relationship with nature and local communities has never been more critical. Ecotourism, as a paradigm of sustainable travel, holds transformative potential—not only for preserving our planet's fragile ecosystems but also for fostering local cultural and economic development. In this context, the integration of circular economy principles offers innovative pathways to enhance sustainability across the tourism sector. The application of circular economy frameworks in tourism not only reduces environmental impact but also enhances economic viability by creating closed-loop systems. My interest in this topic stems from a personal conviction: Tourism should leave a positive mark, one that enriches rather than diminishes the destinations we visit. This study delves into how the hotel industry can align itself with ecotourism principles by embracing innovative, sustainable practices that minimize environmental impact while delivering authentic, high-quality experiences for travelers. Through the lens of green energy, resource optimization, and cultural integration, the research demonstrates that sustainability is both an ethical responsibility and a pathway to long-term competitiveness in tourism. By supporting local economies and protecting natural heritage, the industry can shift from being a passive observer of environmental degradation to a proactive steward of change. This work serves as a call to action for stakeholders: Our choices today will define the landscapes and cultural legacies available to future generations.

This research aims to test the effect that the implementation of green practices at a major sport tourism event, the Badminton World Championships in Huelva (Spain), has on the future intention of spectators to return to similar sport events. A total of 523 spectators who attended the event were randomly selected and self-administered in the presence of the interviewer. A confirmatory factor analysis of the model and a multi-group analysis were carried out. Sporting events have a great impact on the environment in which they are organised, mainly when they are linked to tourism, whether at an economic, social or environmental level. The results indicated that green practices indirectly influence spectators' future intentions through emotions and satisfaction, direct antecedents. In addition, green practices directly affect both image and trust, and indirectly affect satisfaction. In conclusion, green practices are a variable to be taken into account when planning the organisation of a sporting event that aims to consolidate itself in the tourism and sports services market.

The Hungarian tourism and hospitality industry has faced serious challenges in recent years. The tourism and hospitality sector has been confronted with severe challenges in recent years. Even after the end of the pandemic, the industry has not seen the expected recovery, as rising inflation, declining discretionary income and a lack of foreign tourists have further hampered the industry. The hotel market in Budapest in particular has been significantly affected by these developments. Despite the difficulties, investors continue to see opportunities in the market. One example is the purchase by a group of real estate investors of an under-utilised leisure centre in District VII, which they intend to convert into a hotel. Our study is part of this project and its primary objective is to define the parameters of the future hotel and analyse the market opportunities and challenges. Our research focuses on the hotel market in Budapest and uses methods such as benchmarking, STEEP and SWOT analyses, as well as four in-depth interviews with key players in the market. The benchmarking examined the operations of hotels in the capital, while the in-depth interviews provided practical experience and insider perspectives. On the basis of the interviews and analyses, the study identifies possible directions for improvement and factors for competitive advantage.

Brain tumors are a primary factor causing cancer-related deaths globally, and their classification remains a significant research challenge due to the variability in tumor intensity, size, and shape, as well as the similar appearances of different tumor types. Accurate differentiation is further complicated by these factors, making diagnosis difficult even with advanced imaging techniques such as magnetic resonance imaging (MRI). Recent techniques in artificial intelligence (AI), in particular deep learning (DL), have improved the speed and accuracy of medical image analysis, but they still face challenges like overfitting and the need for large annotated datasets. This study addresses these challenges by presenting two approaches for brain tumor classification using MRI images. The first approach involves fine-tuning transfer learning cutting-edge models, including SEResNet, ConvNeXtBase, and ResNet101V2, with global average pooling 2D and dropout layers to minimize overfitting and reduce the need for extensive preprocessing. The second approach leverages the Vision Transformer (ViT), optimized with the AdamW optimizer and extensive data augmentation. Experiments on the BT-Large-4C dataset demonstrate that SEResNet achieves the highest accuracy of 97.96%, surpassing ViT’s 95.4%. These results suggest that fine-tuning and transfer learning models are more effective at addressing the challenges of overfitting and dataset limitations, ultimately outperforming the Vision Transformer and existing state-of-the-art techniques in brain tumor classification.